arXiv subjects

Yue Mao

Publications and source records attributed to Yue Mao.

At least 19 recordsLinked to original sources

arXiv2026

Spin-textured orbitals in altermagnetic artificial atoms

Artificial atoms provide a versatile platform for engineering atomic-like orbitals, yet spin generally remains a passive degree of freedom in their orbital structure. Here, we introduce the concept of altermagnetic artificial atoms formed by confining electrons with momentum-dependent spin splitting. We show that altermagnetism reconstructs conventional confined orbitals into spin-textured orbitals, with spatially distinct distributions of opposite spin components. The resulting confined spectrum retains a twofold degeneracy protected by the combined $C_{4z}\mathcal{T}$ symmetry. These spin textures persist in higher-energy states, where additional radial structures combine with the characteristic angular spin pattern. Furthermore, strain resolves the degenerate orbital pairs into spin-polarized states, and continuously tunes their energy splitting. Our results establish altermagnetic artificial atoms as a route to engineering spin-dependent orbital structures in quantum-confined systems.

cond-mat.mes-hall↗

arXiv2026

Majorana modes in helical altermagnet without net magnetism and spin-orbit coupling

We propose a scheme to realize topological superconductor and Majorana bound states (MBSs) in a one-dimensional metal nanowire on the surface of a helical altermagnet and in proximity to an s-wave superconductor, removing the requirement of conventional spin-orbit coupling and net magnetism. Through gauge transformation, we demonstrate that the helical frame naturally induces spin-momentum locking while the altermagnetism breaks time-reversal symmetry. The topological superconducting phase is well tuned by chemical potential, altermagnet strength, and helical frequency. Besides, our transport calculation results reveal quantized conductance signatures: a 2e2/h zero-bias peak at nanowire ends and a 4e2/h tunneling conductance at the domain wall of nanowires with opposite chirality, detected via metal lead and scanning tunneling microscopy, respectively. Our research offers new perspectives on finding MBSs.

cond-mat.supr-con↗

arXiv2026

Spin transport in a normal meta-altermagnetic superconducting nanowire junction

Spin triplet superconductors are considered a promising platform for dissipationless spin transport, where spin currents are carried by spin triplet Cooper pairs. In this paper, we propose that the spin triplet superconductivity and spin supercurrent can be engineered in an altermagnetic superconducting nanowire, where a one-dimensional nanowire is placed on the surface of an s-wave superconductor and in proximity to the altermagnet. Using the nonequilibrium Green's function method, we demonstrate a nonzero equal spin Andreev reflection coefficient at the normal metal-altermagnetic superconducting nanowire interface, thereby verifying the injection of spin triplet Cooper pairs. Furthermore, we systematically investigate the spin transport properties in this hybrid system under a spin bias. Our results demonstrate that these properties can be effectively tuned by the chemical potential and spin bias orientation. Our proposal provides a pathway toward realizing dissipationless spin transport.

cond-mat.supr-con↗

arXiv2026

Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization

Inverse reinforcement learning (IRL) learns a reward function and a corresponding policy that best fit the demonstration data of an expert. However, in the current IRL setting, the learner is isolated from the expert and can only passively observe the expert demonstrations. This limits the applicability of IRL to interactive settings, where the learner actively interacts with the expert and needs to infer the expert's reward function from the interactions. To bridge the gap, this paper studies interactive IRL (IIRL) where a learner aims to learn the reward function of an expert and a policy to interact with the expert during its interactions with the expert. We formulate IIRL as a stochastic bi-level optimization problem where the lower level learns a reward function to explain the behaviors of the expert, and the upper level learns a policy to interact with the expert. We develop a double-loop algorithm, Bi-level Interactive Scenarios Inverse Reinforcement Learning (BISIRL), which solves the lower-level problem in the inner loop and the upper-level problem in the outer loop. We formally guarantee that BISIRL converges and validate our algorithm through extensive experiments.

cs.LG↗

arXiv2025

Anomalous Magnetoresistance beyond the Julli\`ere Model for Spin Selectivity in Chiral Molecules

The issue of anomalous high magnetoresistance, beyond the Julli\`ere model, observed in nonmagnetic electrode-chiral molecular-ferromagnetic electrode devices has puzzled the community for a long time. Here, by considering the magnetic proximity effect which shifts the nonmagnetic-ferromagnetic interface toward chiral molecules, we show the anomalous high magnetoresistance beyond the spin polarization in ferromagnetic electrodes even in the very weak spin-orbit coupling. Our results are in excellent agreement with the experiments, demonstrating that the spin-orbit coupling plays a fundamental role in chiral-induced spin selectivity and the magnetic proximity effect can dramatically enhance the magnetoresistance. These results elucidate the interaction between chiral molecules and ferromagnetic electrodes and facilitate the design of chiral-based spintronic devices.

physics.chem-ph↗

arXiv2025

Orbital hybridization in graphene-based artificial atoms

Intraatomic orbital hybridization and interatomic bond formation are the two fundamental processes when real atoms are condensed to form matter. Artificial atoms mimic real atoms by demonstrating discrete energy levels attributable to quantum confinement. As such, they offer a solid-state analogue for simulating intraatomic orbital hybridization and interatomic bond formation. Signatures of interatomic bond formation has been extensively observed in various artificial atoms. However, direct evidence of the intraatomic orbital hybridization in the artificial atoms remains to be experimentally demonstrated. Here we, for the first time, realize the orbital hybridization in artificial atoms by altering the shape of the artificial atoms. The anisotropy of the confining potential gives rise to the hybridization between quasibound states with different orbital quantum numbers within the artificial atom. These hybridized orbits are directly visualized in real space in our experiment and are well reproduced by both numerical calculations and analytical derivations. Our study opens an avenue for designing artificial matter that cannot be accessed on real atoms through experiments. Moreover, the results obtained inspire the progressive control of quantum states in diverse systems.

cond-mat.mes-hall↗

arXiv2025

Atomic collapse of high-order singular potentials in graphene

Artificial atoms in graphene hosting a series of quasi-bound states can serve as an excellent platform to explore atomic collapse and become a basis to design novel graphene nanodevices. We theoretically study behaviors of massless Dirac fermions in singular potentials with a general form of 1/r^{\gamma}. Different from the Coulomb potential that demands a supercritical charge Z > Zc, a high-order singular potential ({\gamma} > 1) is found to in principle induce atomic collapse with an infinitesimal charge Z. The energies of atomic collapse states (ACSs) within these potentials are arranged roughly as a power sequence. We also show that some special ACSs can exist even above the bulk Dirac point, which cannot appear in the Coulomb potential. These findings uncover the anomalies of massless Dirac fermions in diverse charge potentials and provide guidance for further experiments and graphene nanodevice applications.

cond-mat.mes-hall↗

arXiv2025

Spin Splitting Nernst Effect in Altermagnet

Altermagnet is a distinctive magnet phase, which has spin-split energy band but with zero net magnetic moment. In this paper, we propose that altermagnet behaves spin splitting Nernst effect: Under a longitudinal temperature gradient, the electrons with opposite spins tend to split oppositely in the transverse direction, thus generating a transverse spin current. The spin splitting Nernst effect is understood from the contribution of the longitudinal wave vector to the transverse group velocity. Using the nonequilibrium Green's function method, we calculate the spin-dependent transmission coefficient in the four-terminal altermagnet device. From the spin-dependent transmission coefficient, the nonzero transverse spin current from longitudinal temperature gradient is obtained, and the spin splitting Nernst effect is verified. We systematically study the parameter dependence of the spin splitting Nernst effect, while also performing symmetry analysis. The spin splitting Nernst effect can be easily regulated by Fermi surface energy, temperature, transport direction, and system size. Furthermore, in altermagnet, the $xy$-response and $yx$-response spin splitting Nernst coefficients are equal with $N_{s,xy}=N_{s,yx}$, different from the conventional spin Nernst effect where they are opposite. Meanwhile, the spin splitting Nernst effect require neither spin-orbit coupling nor net magnetism.

cond-mat.mes-hall↗

arXiv2025

Design of a Josephson diode based on double magnetic impurities

We theoretically propose a universal superconducting diode device based on double magnetic impurities which are coupled to the connection region of the Josephson junction. The positive and negative currents flowing across the junction can generate opposite magnetic fields, flipping the magnetic moment of the side magnetic impurity to the opposite directions, and in turn, the two impurities will have different impacts on the opposite currents. This results in the phenomenon that the positive and negative critical currents are unequal, referred to as the superconducting diode effect (SDE). Using the nonequilibrium Green's function method (NEGF), we obtain the direction-dependent critical currents. We confirm the emergency of the SDE and demonstrate the dependence of the superconducting diode efficiency on a range of parameters including the magnitude, the direction, and the position of the magnetic moment. Besides, we systematically analyze the symmetry relations of the nonreciprocity in our system. Our proposal has high practicability by avoiding the demand on the external magnetic field, the Cooper pair momentum, and the spin-orbit coupling. Our approach opens up new possibilities for the development of nonreciprocal electronic circuits and provides an alternate perspective on the advancement of superconducting devices.

cond-mat.supr-con↗

arXiv2025

Dynamical theory of chiral-induced spin selectivity in electron donor-chiral molecule-acceptor systems

The chiral-induced spin selectivity (CISS) effect, a phenomenon where the chirality of molecules imparts significant spin selectivity to electron transfer processes, has garnered increasing interest among the chemistry, biology, and physics communities. Although this effect was discovered more than a decade ago, the dynamical process of how electron spin polarization is caused by chiral molecules is still unclear. Here, we propose a dynamical theory of electron transfer in donor-chiral molecule bridge-acceptor systems without electrodes or substrates based on the Lindblad-type master equation. We demonstrate that the molecular spin-orbit coupling generates unequal spin velocities and achieves steady spin polarization with the help of dephasing. Our work elucidates the dynamical process of CISS and may promote the applications of chiral-based spintronic devices.

physics.chem-ph↗

arXiv2025

Tunneling Magnetoresistance Effect in Altermagnets

As an unconventional magnet, altermagnetism attracts great interest in condensed matter physics and applies a new research platform for the spintronics. Since the tunneling magnetoresistance (TMR) effect is an important research aspect in spintronics, we theoretically propose a universal altermagnetic sandwich device to achieve the TMR effect and investigate its transport properties. Using the nonequilibrium Green's function method and the Landauer-B\"uttiker formula, we obtain the conductance and the TMR ratio. By systematically rotating the orientations of the altermagnet and spin, we investigate how the altermagnetic orientations affect the conductance and the TMR ratio, and comprehensively demonstrate the dependence of the conductance and the TMR ratio on a range of parameters in the system. By tuning the altermagnetism strength and the Fermi energy, as well as rotating the orientations in the altermagnet, the TMR ratio can reach a value of over 1000%. In addition, we analyze the detailed symmetry relations of the conductance and the TMR ratio in our system. Our approach provides a new design concept for the next-generation information technologies based on the altermagnetic platform, paving the way for the development of spintronics applications.

cond-mat.mes-hall↗

arXiv2025

Phase transitions in quantum dot-Majorana zero mode coupling systems

The magnetic doublet ground state (GS) of a quantum dot (QD) could be changed to a spin-singlet GS by coupling to a superconductor. In analogy, here we study the GS phase transitions in QD-Majorana zero mode (MZM) coupling systems: GS behaves phase transition versus intra-dot energy level and QD-MZM coupling strength. The phase diagrams of GS are obtained, for cases with and without Zeeman term. Along with the phase transition, we also study the change of spin feature and density of states. The properties of the phase transition are understood via a mean-field picture. Our study not only serves as an analogue to QD-superconductor phase transitions, but also gives alternative explanations on MZM-relevant experiments.

cond-mat.mes-hall↗

arXiv2025

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have been proposed to evaluate their code generation abilities, these benchmarks face several critical limitations. First, they often rely on manual annotations, which are time-consuming and difficult to scale across different programming languages and problem complexities. Second, most existing benchmarks focus primarily on Python, while the few multilingual benchmarks suffer from limited difficulty and uneven language distribution. To address these challenges, we propose AutoCodeGen, an automated method for generating high-difficulty multilingual code generation datasets without manual annotations. AutoCodeGen ensures the correctness and completeness of test cases by generating test inputs with LLMs and obtaining test outputs through a multilingual sandbox, while achieving high data quality through reverse-order problem generation and multiple filtering steps. Using this novel method, we introduce AutoCodeBench, a large-scale code generation benchmark comprising 3,920 problems evenly distributed across 20 programming languages. It is specifically designed to evaluate LLMs on challenging, diverse, and practical multilingual tasks. We evaluate over 30 leading open-source and proprietary LLMs on AutoCodeBench and its simplified version AutoCodeBench-Lite. The results show that even the most advanced LLMs struggle with the complexity, diversity, and multilingual nature of these tasks. Besides, we introduce AutoCodeBench-Complete, specifically designed for base models to assess their few-shot code generation capabilities. We hope the AutoCodeBench series will serve as a valuable resource and inspire the community to focus on more challenging and practical multilingual code generation scenarios.

cs.CL↗

arXiv2025

Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought

As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mamba's long-sequence processing efficiency with Transformer's superior contextual understanding. Hunyuan-TurboS features an adaptive long-short chain-of-thought (CoT) mechanism, dynamically switching between rapid responses for simple queries and deep "thinking" modes for complex problems, optimizing computational resources. Architecturally, this 56B activated (560B total) parameter model employs 128 layers (Mamba2, Attention, FFN) with an innovative AMF/MF block pattern. Faster Mamba2 ensures linear complexity, Grouped-Query Attention minimizes KV cache, and FFNs use an MoE structure. Pre-trained on 16T high-quality tokens, it supports a 256K context length and is the first industry-deployed large-scale Mamba model. Our comprehensive post-training strategy enhances capabilities via Supervised Fine-Tuning (3M instructions), a novel Adaptive Long-short CoT Fusion method, Multi-round Deliberation Learning for iterative improvement, and a two-stage Large-scale Reinforcement Learning process targeting STEM and general instruction-following. Evaluations show strong performance: overall top 7 rank on LMSYS Chatbot Arena with a score of 1356, outperforming leading models like Gemini-2.0-Flash-001 (1352) and o4-mini-2025-04-16 (1345). TurboS also achieves an average of 77.9% across 23 automated benchmarks. Hunyuan-TurboS balances high performance and efficiency, offering substantial capabilities at lower inference costs than many reasoning models, establishing a new paradigm for efficient large-scale pre-trained models.

cs.CL↗

arXiv2025

Catching Spinning Table Tennis Balls in Simulation with End-to-End Curriculum Reinforcement Learning

The game of table tennis is renowned for its extremely high spin rate, but most table tennis robots today struggle to handle balls with such rapid spin. To address this issue, we have contributed a series of methods, including: 1. Curriculum Reinforcement Learning (RL): This method helps the table tennis robot learn to play table tennis progressively from easy to difficult tasks. 2. Analysis of Spinning Table Tennis Ball Collisions: We have conducted a physics-based analysis to generate more realistic trajectories of spinning table tennis balls after collision. 3. Definition of Trajectory States: The definition of trajectory states aids in setting up the reward function. 4. Selection of Valid Rally Trajectories: We have introduced a valid rally trajectory selection scheme to ensure that the robot's training is not influenced by abnormal trajectories. 5. Reality-to-Simulation (Real2Sim) Transfer: This scheme is employed to validate the trained robot's ability to handle spinning balls in real-world scenarios. With Real2Sim, the deployment costs for robotic reinforcement learning can be further reduced. Moreover, the trajectory-state-based reward function is not limited to table tennis robots; it can be generalized to a wide range of cyclical tasks. To validate our robot's ability to handle spinning balls, the Real2Sim experiments were conducted. For the specific video link of the experiment, please refer to the supplementary materials.

cs.RO↗

arXiv2024

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large

cs.CL↗

arXiv2024

Design of Josephson diode based on magnetic impurity

We theoretically propose a mechanism to realize the superconducting diode effect (SDE): The current can generate a magnetic field, affecting the magnetic moment of magnetic impurity. When the connection region of the Josephson junction is coupled with the magnetic impurity, the supercurrents in positive and negative directions have different influences on the magnetic moment. This results in a phenomenon that the critical supercurrents in these opposite directions are unequal, which is called SDE. We model the Josephson connection region by a quantum dot. Then the critical supercurrents are investigated by the non-equilibrium Green's function method, and we carry out a detailed symmetry analysis on the supercurrent relations. The calculation results confirm that the SDE does exist in this system. Besides, the SDE is significant in a wide parameter space and can be effectively adjusted in various ways. Our design only demands a magnetic impurity and conventional superconductors. The unconventional finite-momentum Cooper pair and spin-orbit coupling are not required, and there is also no need for the existence of chirality or an external magnetic field. Our work provides a universal device structure for the development of superconducting electronics.

cond-mat.supr-con↗

arXiv2024

Four-terminal graphene-superconductor thermal switch controlled by the superconducting phase difference

We propose a superconducting phase-controlled thermal switch based on a four-terminal graphene-superconductor system. By the coupling of two superconducting leads on a zigzag graphene nanoribbon, both the normal-transmission coefficient and the crossed-Andreev-reflection coefficient, which dominate the thermal conductivity of electrons in the graphene nanoribbon, can be well controlled simultaneously by the phase difference of the superconducting leads. As a result, the thermal conductivity of electrons in the graphene nanoribbon can be tuned and a thermal switching effect appears. Using the nonequilibrium Green's function method, we verify this thermal switching effect numerically. At ambient temperatures less than about one tenth of the superconducting transition temperature, the thermal switching ratio can exceed 2000. The performance of the thermal switch can be regulated by the ambient temperature, and doping or gating can slightly increase the thermal switching ratio. The use of narrower graphene nanoribbons and wider superconducting leads facilitates the obtaining of larger thermal switching ratios. This switching effect of electronic thermal conductance in graphene is expected to be experimentally realized and applied.

cond-mat.mes-hall↗